Lead Software Engineer: Data Engineering
Posted Updated
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As an Engineering Lead at JPMorganChase within Employee Platforms, you are an integral part of an agile team responsible for the hands-on delivery and architecture of secure, stable, scalable data engineering solutions on AWS and Databricks. As a core technical contributor, you own technical direction, develop critical components, drive production reliability, and deliver trusted technology products in support of the firm’s business objectives.
Job responsibilities
As an Engineering Lead at JPMorganChase within Employee Platforms, you are an integral part of an agile team responsible for the hands-on delivery and architecture of secure, stable, scalable data engineering solutions on AWS and Databricks. As a core technical contributor, you own technical direction, develop critical components, drive production reliability, and deliver trusted technology products in support of the firm’s business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Develops secure and high-quality production code, and reviews and debugs code written by others.
- Architects and delivers data platforms and pipelines across ingestion, transformation, curation, and consumption on AWS and Databricks using Spark and Delta.
- Builds reusable Python and/or Scala libraries, frameworks, pipeline templates, and automation.
- Owns operational excellence, including data quality, monitoring and alerting, SLAs, incident triage and RCA, and performance and cost optimization.
- Drives SDLC standards covering CI/CD, testing strategy, secure coding, resiliency patterns, and design and code reviews.
- Leads and mentors engineers and partners with product, analytics, and security teams to deliver roadmaps and execution.
- Uses enterprise-authorized GenAI developer tools such as GitHub Copilot and Claude Code to accelerate refactoring, test generation, and documentation with strong validation and controls.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Required qualifications, capabilities, and skills
- Formal training or certification on data engineering concepts with architecture and production ownership concepts and 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Strong Python coding skills, and deep experience with Databricks and Spark.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Strong experience with AWS and modern lakehouse and data platform patterns.
- Proven technical leadership through architecture decisions, mentoring, and cross-team influence.
Preferred qualifications, capabilities, and skills
- AWS certification and/or Databricks certification.
- Experience with streaming architectures, including event-driven ingestion, near-real-time processing, and operational support for monitoring, alerting, and recovery.